Degradation analysis in the estimation of photometric redshifts from non-representative training sets
arXiv:1804.03805 · doi:10.1093/mnras/sty880
Abstract
We perform an analysis of photometric redshifts estimated by using a non-representative training sets in magnitude space. We use the ANNz2 and GPz algorithms to estimate the photometric redshift both in simulations as well as in real data from the Sloan Digital Sky Survey (DR12). We show that for the representative case, the results obtained by using both algorithms have the same quality, either using magnitudes or colours as input. In order to reduce the errors when estimating the redshifts with a non-representative training set, we perform the training in colour space. We estimate the quality of our results by using a mock catalogue which is split samples cuts in the -band between . We obtain slightly better results with GPz on single point z-phot estimates in the complete training set case, however the photometric redshifts estimated with ANNz2 algorithm allows us to obtain mildly better results in deeper -band cuts when estimating the full redshift distribution of the sample in the incomplete training set case. By using a cumulative distribution function and a Monte-Carlo process, we manage to define a photometric estimator which fits well the spectroscopic distribution of galaxies in the mock testing set, but with a larger scatter. To complete this work, we perform an analysis of the impact on the detection of clusters via density of galaxies in a field by using the photometric redshifts obtained with a non-representative training set.
19 pages, 9 figures. Accepted for publication in MNRAS
References in corpus (59)
- Observational Evidence from Supernovae for an Accelerating Universe and a Cosmological Constant
- Measurements of Omega and Lambda from 42 High-Redshift Supernovae
- First Year Wilkinson Microwave Anisotropy Probe (WMAP) Observations: Determination of Cosmological Parameters
- Simulating the joint evolution of quasars, galaxies and their large-scale distribution
- Detection of the Baryon Acoustic Peak in the Large-Scale Correlation Function of SDSS Luminous Red Galaxies
- The Eleventh and Twelfth Data Releases of the Sloan Digital Sky Survey: Final Data from SDSS-III
- Hierarchical Galaxy Formation
- EAZY: A Fast, Public Photometric Redshift Code
- The clustering of galaxies in the SDSS-III Baryon Oscillation Spectroscopic Survey: Baryon Acoustic Oscillations in the Data Release 10 and 11 galaxy samples
- Accurate photometric redshifts for the CFHT Legacy Survey calibrated using the VIMOS VLT Deep Survey
- Bayesian photometric redshift estimation
- Cosmological Parameters from Observations of Galaxy Clusters
- Galaxy and Mass Assembly (GAMA): survey diagnostics and core data release
- The WiggleZ Dark Energy Survey: mapping the distance-redshift relation with baryon acoustic oscillations
- Measuring and Modelling the Redshift Evolution of Clustering: the Hubble Deep Field North
- Formation of Galaxy Clusters
- Tracing cosmic evolution with clusters of galaxies
- Measuring the Baryon Acoustic Oscillation scale using the SDSS and 2dFGRS
- Cosmic Microwave Background Anisotropies
- Galaxy And Mass Assembly (GAMA): end of survey report and data release 2
- ANNz: estimating photometric redshifts using artificial neural networks
- Cosmological Constraints from the SDSS maxBCG Cluster Catalog
- Gravitational Lensing
- GAMA: towards a physical understanding of galaxy formation
- CFHTLenS: Combined probe cosmological model comparison using 2D weak gravitational lensing
- The dark matter of gravitational lensing
- Measuring with the ROSAT Deep Cluster Survey
- TPZ : Photometric redshift PDFs and ancillary information by using prediction trees and random forests
- The Zurich Extragalactic Bayesian Redshift Analyzer (ZEBRA) and its first application: COSMOS
- PHAT: PHoto-z Accuracy Testing
- Estimating the Redshift Distribution of Faint Galaxy Samples
- ANNz2 - photometric redshift and probability distribution function estimation using machine learning
- Estimating photometric redshifts with artificial neural networks
- Lightcone mock catalogues from semi-analytic models of galaxy formation - I. Construction and application to the BzK colour selection
- Photometric redshift analysis in the Dark Energy Survey Science Verification data
- New constraints on dark energy from the observed growth of the most X-ray luminous galaxy clusters
- Predictions for the CO emission of galaxies from a coupled simulation of galaxy formation and photon dominated regions
- Constraining cosmological parameters using Sunyaev-Zel'dovich cluster surveys
- Photometric redshifts with the Multilayer Perceptron Neural Network: application to the HDF-S and SDSS
- X-ray Clusters of Galaxies as Tracers of Structure in the Universe
- GPz: Non-stationary sparse Gaussian processes for heteroscedastic uncertainty estimation in photometric redshifts
- A Comparison of Six Photometric Redshift Methods Applied to 1.5 Million Luminous Red Galaxies
- Covariance Estimation in High Dimensions via Kronecker Product Expansions
- Photo-z for weak lensing tomography from space: the role of optical and near-IR photometry
- Robust Machine Learning Applied to Astronomical Datasets III: Probabilistic Photometric Redshifts for Galaxies and Quasars in the SDSS and GALEX
- A Sparse Gaussian Process Framework for Photometric Redshift Estimation
- Combining clustering and abundances of galaxy clusters to test cosmology and primordial non-Gaussianity
- On the realistic validation of photometric redshifts, or why Teddy will never be Happy
- Photometric Redshift Error Estimators
- The Voronoi Tessellation cluster finder in 2+1 dimensions
- The Northern Sky Optical Cluster Survey I: Detection of Galaxy Clusters in DPOSS
- Designing Future Dark Energy Space Mission: I. Building Realistic Galaxy Spectro-Photometric Catalogs and their first applications
- New Approaches To Photometric Redshift Prediction Via Gaussian Process Regression In The Sloan Digital Sky Survey
- What Lies Beneath: Using p(z) to Reduce Systematic Photometric Redshift Errors
- A cooperative approach among methods for photometric redshifts estimation: an application to KiDS data
- Photometric redshift estimation using Gaussian processes
- Galaxy and Mass Assembly (GAMA): Colour and luminosity dependent clustering from calibrated photometric redshifts
- The SDSS-IV extended Baryon Oscillation Spectroscopic Survey: selecting emission line galaxies using the Fisher discriminant
- Capability of Quasar Selection by Combining the SCUSS and SDSS Observations
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- Dark Energy Survey Year 3 Results: Calibration of Lens Sample Redshift Distributions using Clustering Redshifts with BOSS/eBOSS
- Bayesian photometric redshifts of blended sources
- Estimation of Photometric Redshifts. I. Machine Learning Inference for Pan-STARRS1 Galaxies Using Neural Networks
- Gaussian Mixture Models for Blended Photometric Redshifts
- Machine learning analysis of Photometric data from the Dark Energy Survey